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Proceedings Paper

A cost effective and high fidelity fluoroscopy simulator using the Image-Guided Surgery Toolkit (IGSTK)
Author(s): Ren Hui Gong; Brad Jenkins; Raymond W. Sze; Ziv Yaniv
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Paper Abstract

The skills required for obtaining informative x-ray fluoroscopy images are currently acquired while trainees provide clinical care. As a consequence, trainees and patients are exposed to higher doses of radiation. Use of simulation has the potential to reduce this radiation exposure by enabling trainees to improve their skills in a safe environment prior to treating patients. We describe a low cost, high fidelity, fluoroscopy simulation system. Our system enables operators to practice their skills using the clinical device and simulated x-rays of a virtual patient. The patient is represented using a set of temporal Computed Tomography (CT) images, corresponding to the underlying dynamic processes. Simulated x-ray images, digitally reconstructed radiographs (DRRs), are generated from the CTs using ray-casting with customizable machine specific imaging parameters. To establish the spatial relationship between the CT and the fluoroscopy device, the CT is virtually attached to a patient phantom and a web camera is used to track the phantom’s pose. The camera is mounted on the fluoroscope’s intensifier and the relationship between it and the x-ray source is obtained via calibration. To control image acquisition the operator moves the fluoroscope as in normal operation mode. Control of zoom, collimation and image save is done using a keypad mounted alongside the device’s control panel. Implementation is based on the Image-Guided Surgery Toolkit (IGSTK), and the use of the graphics processing unit (GPU) for accelerated image generation. Our system was evaluated by 11 clinicians and was found to be sufficiently realistic for training purposes.

Paper Details

Date Published: 12 March 2014
PDF: 11 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 903618 (12 March 2014); doi: 10.1117/12.2044112
Show Author Affiliations
Ren Hui Gong, Stanford Univ. School of Medicine (United States)
Brad Jenkins, Children's National Medical Ctr. (United States)
Raymond W. Sze, Children's National Medical Ctr. (United States)
Ziv Yaniv, Children's National Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes, Editor(s)

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